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Temporal and instantaneous connectivity of default mode network estimated using Gaussian Bayesian network frameworks
Li, Juan1; Li, Rui5; Chen, Kewei1,3,4; Yao, Li1,2; Wu, Xia1,2; Wu, X (reprint author), Beijing Normal Univ, Coll Informat Sci & Technol, Xin Jie Kou Wai St 19, Beijing 100875, Peoples R China.
2012-03-28
Source PublicationNEUROSCIENCE LETTERS
ISSN0304-3940
SubtypeArticle
Volume513Issue:1Pages:62-66
Contribution Rank4
AbstractBy probing its functional anatomy, the default mode network (DMN) can be considered consisting of two interacting hub and non-hub subsystems. The hub subsystem includes posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and bilateral inferior parietal cortex (IPC). The non-hub subsystem contains inferior temporal cortex (ITC) and (para) hippocampus (HC). In this study, Gaussian Bayesian Network (BN) and Gaussian Dynamic Bayesian Network (DBN) were applied separately to detect the instantaneous and temporal connection relationship within each and between the two DMN subsystems. It was found that the directional instantaneous interactions between the two subsystems were primarily "from non-hub to hub". The temporal interactions between hub and non-hub regions, on the other hand, are less presented between the two subsystems. The hub subsystem demonstrated both strong instantaneous and temporal interactions among the hub regions, while the non-hub regions were only strongly inter-connected instantaneously but temporally isolated with each other. In addition, one of the hub regions, PCC, appears to be a confluent node and important in the functional integration within the network. (c) 2012 Elsevier Ireland Ltd. All rights reserved.
KeywordDefault mode network (DMN) Effective connectivity Instantaneous connectivity Temporal connectivity Bayesian networks (BN) Posterior cingulate cortex (PCC)
Subject AreaCognitive Neuroscience
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China [60931003, 60905063] ; Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences [Y1CX251005]
Project Intro.Thanks to the anonymous reviewers for their insightful views and constructive suggestions on the model interpretation. This work was supported by the Key Program (No.60931003), General Program (No.60905063) of National Natural Science Foundation of China and Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (No. Y1CX251005).
WOS IDWOS:000302512800013
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Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/13278
Collection中国科学院心理健康重点实验室
Corresponding AuthorWu, X (reprint author), Beijing Normal Univ, Coll Informat Sci & Technol, Xin Jie Kou Wai St 19, Beijing 100875, Peoples R China.
Affiliation1.Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China
2.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
3.Banner Alzheimers Inst BAI, Phoenix, AZ 85006 USA
4.Banner Good Samaritan PET Ctr, Phoenix, AZ 85006 USA
5.Chinese Acad Sci, Inst Psychol, Key Lab Mental Hlth, Ctr Aging Psychol, Beijing 100875, Peoples R China
Recommended Citation
GB/T 7714
Li, Juan,Li, Rui,Chen, Kewei,et al. Temporal and instantaneous connectivity of default mode network estimated using Gaussian Bayesian network frameworks[J]. NEUROSCIENCE LETTERS,2012,513(1):62-66.
APA Li, Juan,Li, Rui,Chen, Kewei,Yao, Li,Wu, Xia,&Wu, X .(2012).Temporal and instantaneous connectivity of default mode network estimated using Gaussian Bayesian network frameworks.NEUROSCIENCE LETTERS,513(1),62-66.
MLA Li, Juan,et al."Temporal and instantaneous connectivity of default mode network estimated using Gaussian Bayesian network frameworks".NEUROSCIENCE LETTERS 513.1(2012):62-66.
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